Explainable AI for Medical Science: A Comprehensive Survey, Current Challenges, and Possible Directions
Deep Kothadiya, Chintan B. Bhatt, Amjad R. Khan, Anees Ara, Fatima Nayer Khan · Auerbach Publications eBooks · 2025
Explainable artificial intelligence has emerged as a critical field to address the opacity of complex machine learning models. This chapter navigates through the landscape of explainability techniques, methodologies, and applications in artificial intelligence (AI) systems. We delve into model-specific explainability methods, such as decision trees and rule-based systems, and explore model-agnostic approaches like local interpretable model-agnostic explanations and SHapley Additive exPlanations. The chapter extends its focus to visualizations, for both local and global explanations, emphasizing the role of human-understandable representations in fostering trust and comprehension. This chapter discusses the challenges posed by deep learning models and the ongoing efforts to make them more interpretable, balancing the trade-off between complexity and transparency. Ethical considerations, particularly in relation to bias detection and fairness, are addressed, underlining the importance of explainability in ensuring responsible AI deployment. This chapter contributes a comprehensive overview, bridging the theoretical and practical aspects of explainable AI, ultimately serving as a guide for researchers, practitioners, and policymakers navigating the evolving terrain of interpretable AI.